The document summarizes a study that examined how technology acceptance affects e-learning performance in a non-technology intensive course. The study found that students' positive behavioral intention towards technology significantly improved their performance, but their perception of facilitating conditions and the interaction of academic proficiency with both behavioral intention and facilitating conditions were not significant. The study had some limitations but made contributions. It was recommended that similar studies be conducted at more institutions, longitudinally, and examining factors like race, ethnicity, financial background, and technology acceptance of different online tools.

1. Does Technology
Acceptance Affect
E-learning in a
Non-Technology
Intensive Course?
ACED 7990
Analysis of Research in Adult & Career Education
Dorea M. Hardy, M.Ed.

2. Study Summary
1. The Problem & Purpose
2. Theoretical / Conceptual Framework
o Technology Acceptance Model

3. Study Summary
3. Research Hypotheses
H1 An individual with a higher level of technology acceptance will improve
performance.
H1a Individual’s positive perception of facilitating conditions will improve
performance.
H1b Individual’s positive level of behavioral intention will improve performance.
H2 Academic proficiency directly influences the effect of an individual’s degree of
technology acceptance upon performance.
H2a Academic proficiency directly influences the effect of an individual’s perception
of facilitating conditions upon performance.
H2b Academic proficiency directly influences the effect of an individual’s level of
behavioral intention upon performance.

4. Study Summary
4. Research Design & Data Gathering Methods

5. Study Summary
5. Data Analysis Techniques
o Two-Tailed Regression
o Performance = AP + BI + FC + APBI + APFC
6. Findings
o BI p < .0001 positive & significant
o FC p < .89 not significant
o APBI p < .001 negative & significant
o APFC p < .50 not significant
7. Conclusions

6. Strengths
1. Authors’ acknowledgement of limitations
and shortcomings

7. Shortcomings
• Model unproven for specified use
• Sample Population
o Gender Ratio
o Race / Ethnicity *
o Economic / Financial Background *
o Average Age
• Added workload for Instructors
• Single Institution
• Unproctored Quizzes

8. Strengths
2. Literature Contribution
3. Carefully written to prevent loss of readers
o Explained logic behind model utilized
4. Two-Tailed Test with Regression

9. One-Tailed Test
(positive)
Image Source:
http://www.ats.ucla.edu/stat/mult_pkg/faq/general/tail_tests.htm

10. One-Tailed Test
(negative)
Image Source:
http://www.ats.ucla.edu/stat/mult_pkg/faq/general/tail_tests.htm

11. Two-Tailed Test
(positive & negative)
Image Source:
http://www.ats.ucla.edu/stat/mult_pkg/faq/general/tail_tests.htm

12. Strengths
4. Two-Tailed Test with Regression
o Exploratory Nature
o Significant versus Non-Significant
o Reverse of Expectations
5. Recommendations

13. Recommendations
• Authors’ Recommendations
o Additional Institutions (replication)
o Longitudinal Studies
o Technology acceptance & other online tools
o Wider age categories
o Gender focused studies
o Locus of Control & Learning Styles Studies
o Research the underutilization of e-tools by instructors

14. Recommendations
• Personal Recommendations
o Race & Ethnicity Study
• Is there a gap that exists?

15. Recommendations
• Personal Recommendations
o Race & Ethnicity Study
o Financial Backgrounds Study
• What impact does a low income background have on students
compared with peers from a higher income background?
o Lower Technology Acceptance?
o Lower Learning Performance?
• Could results benefit lower income districts?

16. Recommendations
• Personal Recommendations
o Race & Ethnicity Study
o Financial Backgrounds Study
o Regularity of Studies
• Technology Life Cycle

17. Questions, Comments
& Discussion
Presented
April 22, 2014